cellrank.tl.estimators.CFLARE
- class cellrank.tl.estimators.CFLARE(object, **kwargs)[source]
Compute the initial/terminal states of a Markov chain via spectral heuristics.
This estimator uses the left eigenvectors of the transition matrix to filter to a set of recurrent cells and the right eigenvectors to cluster this set of cells into discrete groups.
- Parameters
object (
Union
[AnnData
,ndarray
,spmatrix
,KernelExpression
]) –Can be one of the following types:
anndata.AnnData
- annotated data object.scipy.sparse.spmatrix
,numpy.ndarray
- row-normalized transition matrix.cellrank.tl.kernels.KernelExpression
- kernel expression.str
- key inanndata.AnnData.obsp
where the transition matrix is stored.adata
must be provided in this case.bool
- directionality of the transition matrix that will be used to infer its storage location. If None, the directionality will be determined automatically.adata
must be provided in this case.
kwargs (
Any
) – Keyword arguments forcellrank.tl.kernels.PrecomputedKernel
.
Attributes
Absorption probabilities.
Mean and variance of the time until absorption.
Annotated data object.
Direction of
kernel
.Eigendecomposition of
transition_matrix
.Underlying kernel expression.
Potential lineage drivers.
Estimator parameters.
Priming degree.
Shape of the kernel.
Categorical annotation of terminal states.
Aggregated probability of cells to be in terminal states.
Transition matrix of
kernel
.Methods
compute_absorption_probabilities
([keys, ...])Compute absorption probabilities.
compute_eigendecomposition
([k, which, ...])Compute eigendecomposition of
transition_matrix
.compute_lineage_drivers
([lineages, method, ...])Compute driver genes per lineage.
compute_lineage_priming
([method, early_cells])Compute the degree of lineage priming.
compute_terminal_states
(*args, **kwargs)Compute terminal states of the process.
copy
(*[, deep])Return a copy of self.
fit
([k])Prepare self for terminal states prediction.
from_adata
(adata, obsp_key)Deserialize self from
anndata.AnnData
.plot_absorption_probabilities
([states, ...])Plot continuous or categorical observations in an embedding or along pseudotime.
plot_lineage_drivers
(lineage[, n_genes, ...])Plot lineage drivers discovered by
compute_lineage_drivers()
.plot_lineage_drivers_correlation
(lineage_x, ...)Show scatter plot of gene-correlations between two lineages.
plot_spectrum
([n, real_only, show_eigengap, ...])Plot the top eigenvalues in real or complex plane.
plot_terminal_states
([states, color, ...])Plot continuous or categorical observations in an embedding or along pseudotime.
predict
([use, percentile, method, ...])Find approximate recurrent classes of the Markov chain.
read
(fname[, adata, copy])Deserialize self from a file.
rename_terminal_states
(new_names)Rename categories in
terminal_states
.set_terminal_states
(labels[, cluster_key, ...])Manually define terminal states.
to_adata
([keep, copy])Serialize self to
anndata.Anndata
.write
(fname[, write_adata, ext])Serialize self to a file.